{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "b0c4ee7c-4910-4296-a9a9-5b56e7eca33d",
   "metadata": {
    "tags": []
   },
   "source": [
    "## 作业要求\n",
    "\n",
    "1. 读取“scores.csv”中的数据。\n",
    "2. 计算每个科的平均成绩。\n",
    "3. 绘制所有科目平均成绩的雷达图。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "f5ef8a1d-2479-4f81-be03-1a2139276af2",
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "76c75912-4473-4352-b632-6bc5e48c6a2a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>chn</th>\n",
       "      <th>math</th>\n",
       "      <th>eng</th>\n",
       "      <th>phy</th>\n",
       "      <th>chem</th>\n",
       "      <th>politics</th>\n",
       "      <th>bio</th>\n",
       "      <th>history</th>\n",
       "      <th>geo</th>\n",
       "      <th>pe</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>99.0</td>\n",
       "      <td>120</td>\n",
       "      <td>114.0</td>\n",
       "      <td>70.0</td>\n",
       "      <td>49.50</td>\n",
       "      <td>50.0</td>\n",
       "      <td>49.0</td>\n",
       "      <td>48.5</td>\n",
       "      <td>49.5</td>\n",
       "      <td>60</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>107.0</td>\n",
       "      <td>120</td>\n",
       "      <td>118.5</td>\n",
       "      <td>68.6</td>\n",
       "      <td>43.00</td>\n",
       "      <td>49.0</td>\n",
       "      <td>48.5</td>\n",
       "      <td>48.5</td>\n",
       "      <td>49.0</td>\n",
       "      <td>56</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>98.0</td>\n",
       "      <td>120</td>\n",
       "      <td>116.0</td>\n",
       "      <td>70.0</td>\n",
       "      <td>47.50</td>\n",
       "      <td>47.0</td>\n",
       "      <td>49.0</td>\n",
       "      <td>47.5</td>\n",
       "      <td>49.0</td>\n",
       "      <td>60</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>102.0</td>\n",
       "      <td>113</td>\n",
       "      <td>111.5</td>\n",
       "      <td>70.0</td>\n",
       "      <td>47.00</td>\n",
       "      <td>49.0</td>\n",
       "      <td>49.0</td>\n",
       "      <td>49.0</td>\n",
       "      <td>49.5</td>\n",
       "      <td>60</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>103.0</td>\n",
       "      <td>120</td>\n",
       "      <td>111.5</td>\n",
       "      <td>70.0</td>\n",
       "      <td>44.75</td>\n",
       "      <td>46.5</td>\n",
       "      <td>48.0</td>\n",
       "      <td>48.0</td>\n",
       "      <td>48.0</td>\n",
       "      <td>60</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     chn  math    eng   phy   chem  politics   bio  history   geo  pe\n",
       "0   99.0   120  114.0  70.0  49.50      50.0  49.0     48.5  49.5  60\n",
       "1  107.0   120  118.5  68.6  43.00      49.0  48.5     48.5  49.0  56\n",
       "2   98.0   120  116.0  70.0  47.50      47.0  49.0     47.5  49.0  60\n",
       "3  102.0   113  111.5  70.0  47.00      49.0  49.0     49.0  49.5  60\n",
       "4  103.0   120  111.5  70.0  44.75      46.5  48.0     48.0  48.0  60"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = pd.read_csv(\"scores.csv\").drop([\"num\", \"class\"], axis=1)\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "d3d4906a-2117-4864-b80f-2d8e299c6c37",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "chn         83.240401\n",
       "math        93.984975\n",
       "eng         85.547412\n",
       "phy         54.151252\n",
       "chem        34.632304\n",
       "politics    41.999165\n",
       "bio         42.174457\n",
       "history     36.821369\n",
       "geo         43.915693\n",
       "pe          53.863105\n",
       "dtype: float64"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "meanScorces = data.mean()\n",
    "meanScorces"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "72fb7b3c-1be8-4bd0-9687-f4780ceab5a8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([83.24040067, 93.98497496, 85.54741235, 54.15125209, 34.63230384,\n",
       "       41.99916528, 42.17445743, 36.82136895, 43.91569282, 53.86310518,\n",
       "       83.24040067])"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "values = np.append(meanScorces, meanScorces[0])\n",
    "values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "db1b1840-f8b7-41dd-a28a-01ca60b50702",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "theta = np.linspace(0, 2 * np.pi, 11)\n",
    "plt.polar(theta, values)\n",
    "plt.fill(theta, values)\n",
    "plt.xticks(theta[0:10], meanScorces.index.tolist())\n",
    "plt.show()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.10.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}
